All tags
Topic: "agent-design"
not much happened today
claude-fable-5 muse-image muse-video audex anthropic langchain google meta-ai-fair nvidia cohere weaviate agent-design background-execution task-management human-in-the-loop agentic-generation reinforcement-learning model-scaling moe context-windows audio-processing video-generation image-generation open-source model-release mikeyk kimmonismus lilian_weng sakana _philschmid officiallogank dimillian reach_vb teknuim victorialslocum omarsar0 alexandr_wang _tim_brooks
Anthropic expanded the "background agent" UX with Claude Cowork for mobile and web, emphasizing task-running background teammates. They also extended access to Claude Fable 5 on paid plans. The concept of a harness in agent design gained traction, highlighted by Lilian Weng and echoed by LangChain with a new Deep Agents course and open-source project. Google's Gemini API Managed Agents introduced features like background execution and custom function calling. Operator-facing agent infrastructure saw updates from Codex Mobile iOS, Hermes Agent with 1Password integration, and Weaviate 1.38 enabling runtime-gated write access. Experimentation with human-in-the-loop control via phone/SMS was noted. In model releases, Meta AI launched Muse Image and previewed Muse Video, featuring an agentic generation loop with planning, web search, and self-refinement, achieving top ranks on Image and Video Arena. NVIDIA released Audex, a 30B parameter MoE model with 1M context for unified text and audio tasks.
OpenAI takes on Gemini's Deep Research
o3 o3-mini-high o3-deep-research-mini openai google-deepmind nyu uc-berkeley hku reinforcement-learning benchmarking inference-speed model-performance reasoning test-time-scaling agent-design sama danhendrycks ethan-mollick dan-shipper
OpenAI released the full version of the o3 agent, with a new Deep Research variant showing significant improvements on the HLE benchmark and achieving SOTA results on GAIA. The release includes an "inference time scaling" chart demonstrating rigorous research, though some criticism arose over public test set results. The agent is noted as "extremely simple" and currently limited to 100 queries/month, with plans for a higher-rate version. Reception has been mostly positive, with some skepticism. Additionally, advances in reinforcement learning were highlighted, including a simple test-time scaling technique called budget forcing that improved reasoning on math competitions by 27%. Researchers from Google DeepMind, NYU, UC Berkeley, and HKU contributed to these findings. The original Gemini Deep Research team will participate in the upcoming AI Engineer NYC event.